{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "e017c9a3",
   "metadata": {},
   "source": [
    "# Learning to Control an Energy Storage System in NeuroMANCER\n",
    "\n",
    "This tutorial demonstrates the use of [Differentiable predictive control (DPC)](https://www.sciencedirect.com/science/article/pii/S0959152422000981) method to learn constrained neural policy for controlling [pumped-storage hydroelectricity](https://en.wikipedia.org/wiki/Pumped-storage_hydroelectricity) system described by [nonlinear ordinary differential equations (ODE)](https://en.wikipedia.org/wiki/Ordinary_differential_equation).\n",
    "\n",
    "\n",
    "## Pumped-storage Hyrdoelectricity System\n",
    "\n",
    "Lets consider a [pumped-storage hydroelectricity](https://en.wikipedia.org/wiki/Pumped-storage_hydroelectricity) (PSH) system which is a type of hydroelectric energy storage used by electric power systems for load balancing. \n",
    "[Load balancing](https://en.wikipedia.org/wiki/Load_balancing_(electrical_power)) or daily peak demand reserve refers to the use of various techniques by electrical power stations to store excess electrical power during low demand periods for release as demand rises. These techniques are an important part of modern power system that help to balance the time-varying load with the generation.\n",
    "As of 2020, the largest form of [grid energy storage]((https://en.wikipedia.org/wiki/Grid_energy_storage)) is dammed hydroelectricity, with both conventional hydroelectric generation as well as pumped-storage hydroelectricity.\n",
    "\n",
    "<img src=\"./figs/PSH.PNG\" width=\"600\">  \n",
    "\n",
    "image adopted from: https://www.upsbatterycenter.com/blog/pumped-storage-hydroelectricity/\n",
    "\n",
    "\n",
    "**System schematics**:  \n",
    "<img src=\"../figs/two_tank_level.png\" width=\"250\">  \n",
    "\n",
    "**System model**:  \n",
    "A simplified system dynamics of PSH system is defined by following nonlinear ordinary differential equations (ODEs):\n",
    "$$\n",
    " \\frac{dx_1}{dt} = c_1 (1.0 - v)  p - c_2  \\sqrt{x_1}  \\\\  \n",
    " \\frac{dx_2}{dt}  = c_1 v p + c_2  \\sqrt{x_1} - c_2 \\sqrt{x_2}\n",
    "$$  \n",
    "With system states $x_1$, and $x_2$ representing liquid levels in tank 1 and 2, respectively. Control actions are pump modulation $p$, and valve opening $v$. The ODE system is parametrized by inlet and outlet valve coefficients $c_1$ and $c_2$, respectively.\n",
    "\n",
    "System model and image adopted from: https://apmonitor.com/do/index.php/Main/LevelControl\n",
    "\n",
    "\n",
    "\n",
    "## Differentiable Predictive Control \n",
    "\n",
    "[Differentiable predictive control (DPC)](https://www.sciencedirect.com/science/article/pii/S0959152422000981) is a model-based offline policy optimization algorithm for learning constrained control policies for dynamical systems.\n",
    "\n",
    "**Control ojective**:   \n",
    "The objective is to control the tank levels of the PSH system into desired reference values by modulating the pump and valve control actions.\n",
    "\n",
    "**Schematics of the Differentiable Predictive Control method**:  \n",
    "<img src=\"../control/figs/DPC_simple_method.png\" width=\"600\">  \n",
    "\n",
    "**Neural control policy**:  \n",
    "The objective of this tutorial is to learn neural control policy $u_k = \\pi(x_k, R)$ to control the tank levels by modulating the pump and valve control actions $u_k = [p_k, v_k]$. The policy takes in the measurements of system states $x_k$ at thime $k$, prediciton of desired references $R = [r_k, ..., r_{k+N}]$ over pre-defined horizon $N$.\n",
    "\n",
    "**Differentiable system model**:  \n",
    "The DPC is a model-based policy optimization algorithm, that exploits the differentiability of a wide class of model representations for dynamical systems, including differential equations, state-space models, or various neural network architectures. In this example, we compactly represent the system model by ODE equations  $\\text{ODESolve}(f(x^i_k, u^i_k))$  describing the governing dynamics of the controlled system. \n",
    "\n",
    "**Differentiable predictive control problem formulation**:  \n",
    "We learn the explicit neural control policy by solving the following parametric optimal control problem: \n",
    "$$\n",
    "\\begin{align}\n",
    "&\\underset{\\theta}{\\text{minimize}}     && \\sum_{i=1}^m  \\Big( \\sum_{k=1}^{N-1} Q_x||x^i_k - r^i_k||_2^2  + Q_N||x^i_N - r^i_N||_2^2 \\Big) \\\\\n",
    "&\\text{subject to}    && x^i_{k+1} =  \\text{ODESolve}(f(x^i_k, u^i_k)) \\\\\n",
    "&                     && u^i_k = \\pi_{\\theta}(x^i_k, R^i) \\\\\n",
    "&                     && 0 \\le x^i_k \\le 1 \\\\\n",
    "&                     && 0 \\le u^i_k \\le 1 \\\\\n",
    "&                     && x^i_0 \\sim \\mathcal{P}_{x_0} \\\\\n",
    "&                     && R^i \\sim  \\mathcal{P}_R\n",
    "\\end{align}\n",
    "$$  \n",
    "The objective function is to minimize the reference tracking error $||x^i_k - r^i_k||_2^2$ over pre-defined prediction horizon $N$ weighted by a scalar $Q_x$, including terminal penalty weighted by $Q_N$.  The parametric neural control policy is given by $\\pi_{\\theta}(x^i_k, R^i)$. The neural control policy is optimized over a problem parameters sampled from the distributions $\\mathcal{P}_{x_0}$, and $\\mathcal{P}_R$, for state initial conditions, and references, respectively. The parameters $\\theta$ are optimized with stochastic gradient descent.\n",
    "\n",
    "\n",
    "### References\n",
    "[1] [Ján Drgoňa, Karol Kiš, Aaron Tuor, Draguna Vrabie, Martin Klaučo,\n",
    "Differentiable predictive control: Deep learning alternative to explicit model predictive control for unknown nonlinear systems,\n",
    "Journal of Process Control, Volume 116, 2022](https://www.sciencedirect.com/science/article/pii/S0959152422000981)  \n",
    "[2] [Jan Drgona, Aaron Tuor, Draguna Vrabie, Learning Constrained Adaptive Differentiable Predictive Control Policies With Guarantees, 2020, arXiv:2004.11184](https://arxiv.org/abs/2004.11184)  \n",
    "[3] Example inspired by: https://apmonitor.com/do/index.php/Main/LevelControl\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff3ffdcd",
   "metadata": {},
   "source": [
    "## NeuroMANCER and Dependencies"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9bac7825",
   "metadata": {},
   "source": [
    "### Install (Colab only)\n",
    "Skip this step when running locally."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "aa12921d",
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install \"neuromancer[examples] @ git+https://github.com/pnnl/neuromancer.git@master\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "1a9b6ec8",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import numpy as np\n",
    "\n",
    "import neuromancer.psl as psl\n",
    "from neuromancer.system import Node, System\n",
    "from neuromancer.modules import blocks\n",
    "from neuromancer.modules.activations import activations\n",
    "from neuromancer.dataset import DictDataset\n",
    "from neuromancer.constraint import variable\n",
    "from neuromancer.loss import PenaltyLoss\n",
    "from neuromancer.problem import Problem\n",
    "from neuromancer.trainer import Trainer\n",
    "from neuromancer.dynamics import ode, integrators\n",
    "from neuromancer.plot import pltCL, pltPhase"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bfe1b7ed",
   "metadata": {},
   "source": [
    "## PHS system model to be controlled"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "cd824de1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# ground truth system model\n",
    "gt_model = psl.nonautonomous.TwoTank()\n",
    "# sampling rate\n",
    "ts = gt_model.params[1]['ts']\n",
    "# problem dimensions\n",
    "nx = gt_model.nx    # number of states\n",
    "nu = gt_model.nu    # number of control inputs\n",
    "nref = nx           # number of references\n",
    "# constraints bounds\n",
    "umin = 0\n",
    "umax = 1.\n",
    "xmin = 0\n",
    "xmax = 1."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fa938158",
   "metadata": {},
   "source": [
    "## Training dataset generation\n",
    "\n",
    "For a training dataset we randomly sample initial conditions of states and sequence of admissible reference trajectories over predefined prediction horizon from given distributions $\\mathcal{P}_{x_0}$, and $\\mathcal{P}_R$, respectively."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "b74ee8c4",
   "metadata": {},
   "outputs": [],
   "source": [
    "nsteps = 50  # prediction horizon\n",
    "n_samples = 2000    # number of sampled scenarios\n",
    "\n",
    "#  sampled references for training the policy\n",
    "list_refs = [torch.rand(1, 1)*torch.ones(nsteps+1, nref) for k in range(n_samples)]\n",
    "ref = torch.cat(list_refs)\n",
    "batched_ref = ref.reshape([n_samples, nsteps+1, nref])\n",
    "# Training dataset\n",
    "train_data = DictDataset({'x': torch.rand(n_samples, 1, nx),   # sampled initial conditions of states\n",
    "                          'r': batched_ref}, name='train')\n",
    "\n",
    "# sampled references for development set\n",
    "list_refs = [torch.rand(1, 1)*torch.ones(nsteps+1, nref) for k in range(n_samples)]\n",
    "ref = torch.cat(list_refs)\n",
    "batched_ref = ref.reshape([n_samples, nsteps+1, nref])\n",
    "# Development dataset\n",
    "dev_data = DictDataset({'x': torch.rand(n_samples, 1, nx),    # sampled initial conditions of states\n",
    "                        'r': batched_ref}, name='dev')\n",
    "\n",
    "# torch dataloaders\n",
    "batch_size = 200\n",
    "train_loader = torch.utils.data.DataLoader(train_data, batch_size=batch_size,\n",
    "                                           collate_fn=train_data.collate_fn,\n",
    "                                           shuffle=False)\n",
    "dev_loader = torch.utils.data.DataLoader(dev_data, batch_size=batch_size,\n",
    "                                         collate_fn=dev_data.collate_fn,\n",
    "                                         shuffle=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0387d6a2",
   "metadata": {},
   "source": [
    "## Differentiable system model and control policy in Neuromancer\n",
    "\n",
    "Here we construct a closed-loop system as differentiable computational graph by coinnecting the system dynamics model  $x_{k+1} = \\text{ODESolve}(f(x_k, u_k))$ with neural control policy $u_k = \\pi_{\\theta}(x_k, R)$. Hence we obtain a trainable system architecture: \n",
    "$x_{k+1} = \\text{ODESolve}(f(x_k, \\pi_{\\theta}(x_k, R)))$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "149f7bc8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# white-box ODE model with no-plant model mismatch\n",
    "two_tank_ode = ode.TwoTankParam()                   # ODE system equations implemented in PyTorch\n",
    "two_tank_ode.c1 = nn.Parameter(torch.tensor(gt_model.c1), requires_grad=False)\n",
    "two_tank_ode.c2 = nn.Parameter(torch.tensor(gt_model.c2), requires_grad=False)\n",
    "\n",
    "# integrate continuous time ODE\n",
    "integrator = integrators.RK4(two_tank_ode, h=torch.tensor(ts))   # using 4th order runge kutta integrator\n",
    "# symbolic system model\n",
    "model = Node(integrator, ['x', 'u'], ['x'], name='model')\n",
    "\n",
    "# neural net control policy with hard control action bounds\n",
    "net = blocks.MLP_bounds(insize=nx + nref, outsize=nu, hsizes=[32, 32],\n",
    "                    nonlin=activations['gelu'], min=umin, max=umax)\n",
    "policy = Node(net, ['x', 'r'], ['u'], name='policy')\n",
    "\n",
    "# closed-loop system model\n",
    "cl_system = System([policy, model], nsteps=nsteps)\n",
    "# cl_system.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "47b33d60",
   "metadata": {},
   "source": [
    "## Differentiable Predictive Control objectives and constraints\n",
    "\n",
    "Here we take advantage of Neuromancer's high level symbolic language to define objective and constraint terms of our optimal control problem."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "3eccb0de",
   "metadata": {},
   "outputs": [],
   "source": [
    "# variables\n",
    "x = variable('x')\n",
    "ref = variable(\"r\")\n",
    "# objectives\n",
    "regulation_loss = 5. * ((x == ref) ^ 2)  # target posistion\n",
    "# constraints\n",
    "state_lower_bound_penalty = 10.*(x > xmin)\n",
    "state_upper_bound_penalty = 10.*(x < xmax)\n",
    "terminal_lower_bound_penalty = 10.*(x[:, [-1], :] > ref-0.01)\n",
    "terminal_upper_bound_penalty = 10.*(x[:, [-1], :] < ref+0.01)\n",
    "# objectives and constraints names for nicer plot\n",
    "regulation_loss.name = 'state_loss'\n",
    "state_lower_bound_penalty.name = 'x_min'\n",
    "state_upper_bound_penalty.name = 'x_max'\n",
    "terminal_lower_bound_penalty.name = 'y_N_min'\n",
    "terminal_upper_bound_penalty.name = 'y_N_max'\n",
    "# list of constraints and objectives\n",
    "objectives = [regulation_loss]\n",
    "constraints = [\n",
    "    state_lower_bound_penalty,\n",
    "    state_upper_bound_penalty,\n",
    "    terminal_lower_bound_penalty,\n",
    "    terminal_upper_bound_penalty,\n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c6d77bde",
   "metadata": {},
   "source": [
    "## Differentiable optimal control problem \n",
    "\n",
    "Here we put things together to construct a differentibale optimal control problem."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "7ab1f85c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# data (x_k, r_k) -> parameters (xi_k) -> policy (u_k) -> dynamics (x_k+1)\n",
    "components = [cl_system]\n",
    "# create constrained optimization loss\n",
    "loss = PenaltyLoss(objectives, constraints)\n",
    "# construct constrained optimization problem\n",
    "problem = Problem(components, loss)\n",
    "# plot computational graph\n",
    "# problem.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb5ef578",
   "metadata": {},
   "source": [
    "## Solve the problem\n",
    "\n",
    "We solve the problem using stochastic gradient descent over pre-defined training data of sampled parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "9467aec0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "epoch: 1  train_loss: 4.087269306182861\n",
      "epoch: 2  train_loss: 3.7248501777648926\n",
      "epoch: 3  train_loss: 3.369007110595703\n",
      "epoch: 4  train_loss: 2.8661580085754395\n",
      "epoch: 5  train_loss: 2.155585765838623\n",
      "epoch: 6  train_loss: 1.3721717596054077\n",
      "epoch: 7  train_loss: 0.9322348833084106\n",
      "epoch: 8  train_loss: 0.854601263999939\n",
      "epoch: 9  train_loss: 0.7949364185333252\n",
      "epoch: 10  train_loss: 0.7383683919906616\n",
      "epoch: 11  train_loss: 0.6969138383865356\n",
      "epoch: 12  train_loss: 0.6607877612113953\n",
      "epoch: 13  train_loss: 0.6246308088302612\n",
      "epoch: 14  train_loss: 0.5888303518295288\n",
      "epoch: 15  train_loss: 0.5548143982887268\n",
      "epoch: 16  train_loss: 0.5212010145187378\n",
      "epoch: 17  train_loss: 0.4884212613105774\n",
      "epoch: 18  train_loss: 0.4576515257358551\n",
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      "epoch: 21  train_loss: 0.38826608657836914\n",
      "epoch: 22  train_loss: 0.37260156869888306\n",
      "epoch: 23  train_loss: 0.35932669043540955\n",
      "epoch: 24  train_loss: 0.3475266396999359\n",
      "epoch: 25  train_loss: 0.3369635343551636\n",
      "epoch: 26  train_loss: 0.3271859288215637\n",
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      "epoch: 28  train_loss: 0.31056326627731323\n",
      "epoch: 29  train_loss: 0.3031129837036133\n",
      "epoch: 30  train_loss: 0.2964351773262024\n",
      "epoch: 31  train_loss: 0.29065871238708496\n",
      "epoch: 32  train_loss: 0.2850676476955414\n",
      "epoch: 33  train_loss: 0.27969446778297424\n",
      "epoch: 34  train_loss: 0.27513575553894043\n",
      "epoch: 35  train_loss: 0.27089589834213257\n",
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      "epoch: 66  train_loss: 0.22652964293956757\n",
      "epoch: 67  train_loss: 0.22606459259986877\n",
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      "epoch: 72  train_loss: 0.2233034074306488\n",
      "epoch: 73  train_loss: 0.2229599952697754\n",
      "epoch: 74  train_loss: 0.22248700261116028\n",
      "epoch: 75  train_loss: 0.22209496796131134\n",
      "epoch: 76  train_loss: 0.2218242585659027\n",
      "epoch: 77  train_loss: 0.22124572098255157\n",
      "epoch: 78  train_loss: 0.2213544398546219\n",
      "epoch: 79  train_loss: 0.22062177956104279\n",
      "epoch: 80  train_loss: 0.21979805827140808\n",
      "epoch: 81  train_loss: 0.21938931941986084\n",
      "epoch: 82  train_loss: 0.21898047626018524\n",
      "epoch: 83  train_loss: 0.2186526358127594\n",
      "epoch: 84  train_loss: 0.21829982101917267\n",
      "epoch: 85  train_loss: 0.21800212562084198\n",
      "epoch: 86  train_loss: 0.21815907955169678\n",
      "epoch: 87  train_loss: 0.21733304858207703\n",
      "epoch: 88  train_loss: 0.21678808331489563\n",
      "epoch: 89  train_loss: 0.21630963683128357\n",
      "epoch: 90  train_loss: 0.21586742997169495\n",
      "epoch: 91  train_loss: 0.21558713912963867\n",
      "epoch: 92  train_loss: 0.2153526097536087\n",
      "epoch: 93  train_loss: 0.2151634693145752\n",
      "epoch: 94  train_loss: 0.21505221724510193\n",
      "epoch: 95  train_loss: 0.21456511318683624\n",
      "epoch: 96  train_loss: 0.2141476571559906\n",
      "epoch: 97  train_loss: 0.2138119637966156\n",
      "epoch: 98  train_loss: 0.21334318816661835\n",
      "epoch: 99  train_loss: 0.21319715678691864\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<All keys matched successfully>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "optimizer = torch.optim.AdamW(problem.parameters(), lr=0.002)\n",
    "#  Neuromancer trainer\n",
    "trainer = Trainer(\n",
    "    problem,\n",
    "    train_loader, dev_loader,\n",
    "    optimizer=optimizer,\n",
    "    epochs=100,\n",
    "    train_metric='train_loss',\n",
    "    eval_metric='dev_loss',\n",
    "    warmup=50,\n",
    ")\n",
    "# Train control policy\n",
    "best_model = trainer.train()\n",
    "# load best trained model\n",
    "trainer.model.load_state_dict(best_model)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c1360ed",
   "metadata": {},
   "source": [
    "# Evaluate best model on a system rollout \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "937b9143",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 2000x1600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nsteps = 750\n",
    "step_length = 150\n",
    "# generate reference\n",
    "np_refs = psl.signals.step(nsteps+1, 1, min=xmin, max=xmax, randsteps=5)\n",
    "R = torch.tensor(np_refs, dtype=torch.float32).reshape(1, nsteps+1, 1)\n",
    "torch_ref = torch.cat([R, R], dim=-1)\n",
    "# generate initial data for closed loop simulation\n",
    "data = {'x': torch.rand(1, 1, nx, dtype=torch.float32),\n",
    "        'r': torch_ref}\n",
    "cl_system.nsteps = nsteps\n",
    "# perform closed-loop simulation\n",
    "trajectories = cl_system(data)\n",
    "\n",
    "# constraints bounds\n",
    "Umin = umin * np.ones([nsteps, nu])\n",
    "Umax = umax * np.ones([nsteps, nu])\n",
    "Xmin = xmin * np.ones([nsteps+1, nx])\n",
    "Xmax = xmax * np.ones([nsteps+1, nx])\n",
    "# plot closed loop trajectories\n",
    "pltCL(Y=trajectories['x'].detach().reshape(nsteps + 1, nx),\n",
    "      R=trajectories['r'].detach().reshape(nsteps + 1, nref),\n",
    "      U=trajectories['u'].detach().reshape(nsteps, nu),\n",
    "      Umin=Umin, Umax=Umax, Ymin=Xmin, Ymax=Xmax,\n",
    "      figname='cl.png')\n",
    "# plot phase portrait\n",
    "pltPhase(X=trajectories['x'].detach().reshape(nsteps + 1, nx),\n",
    "         figname='phase.png')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fd18fbb3",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f59b909f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1189bb9f",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "neuromancer",
   "language": "python",
   "name": "neuromancer"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
